Estimation of prediction error by using K-fold cross-validation

Authors:
Tadayoshi Fushiki
Affiliations:
The Institute of Statistical Mathematics, Tachikawa, Japan 190-8562
Venue:
Statistics and Computing
Year:
2011

Citing 2
Cited 1

Bias correction of cross-validation criterion based on Kullback-Leibler information under a general condition

Journal of Multivariate Analysis - Special issue dedicated to Professor Yasunori Fujikoshi
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Information Criteria and Statistical Modeling

NSGA-II-trained neural network approach to the estimation of prediction intervals of scale deposition rate in oil & gas equipment

Expert Systems with Applications: An International Journal

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Abstract

Estimation of prediction accuracy is important when our aim is prediction. The training error is an easy estimate of prediction error, but it has a downward bias. On the other hand, K-fold cross-validation has an upward bias. The upward bias may be negligible in leave-one-out cross-validation, but it sometimes cannot be neglected in 5-fold or 10-fold cross-validation, which are favored from a computational standpoint. Since the training error has a downward bias and K-fold cross-validation has an upward bias, there will be an appropriate estimate in a family that connects the two estimates. In this paper, we investigate two families that connect the training error and K-fold cross-validation.